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All Journal KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer)
Cici Astria
STIKOM Tunas Bangsa, Pematangsiantar
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Implementasi Inferensi Fuzzy Tsukamoto pada Prediksi Penjualan Telur Ayam Eropa pada Bisnis Raffa Telur Cici Astria; Harly Okprana; Anjar Wanto; Dedy Hartama; Heru Satria Tambunan
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2587

Abstract

Eggs are an animal product that comes from poultry. Eggs are known as a food that contains nutrients that are very good for the body because they contain a high protein source. Apart from being nutritious, people consume a lot of eggs because the price is relatively cheaper than other protein foods. This research was conducted on the Raffa Egg business located in Pematangsiantar City. The data collection process was carried out by means of interviews and observations with Raffa Eggs. This study aims to predict the number of purchases of European chicken eggs from suppliers. The research was conducted using Tsukamoto fuzzy logic with 3 variables, namely sales (x), inventory (y) and purchases (z). where the sales variable (x) consists of 2 fuzzy sets, including increasing and decreasing, inventory (y) consisting of many and few fuzzy sets and purchasing (z) consisting of many and few fuzzy sets. The results of the calculation of the prediction of the number of purchases of European chicken eggs with sales of 6500 and inventory of 25 000 are 29583 items.Keywords: Eggs, European chicken eggs, Fuzzy, Tsukamoto, Pematangsiantar
Pemilihan Model Arsitektur Terbaik Dengan Mengoptimasi Learning Rate Pada Neural Network Backpropagation Cici Astria; Agus Perdana Windarto; Irfan Sudahri Damanik
JURIKOM (Jurnal Riset Komputer) Vol 9, No 1 (2022): Februari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i1.3834

Abstract

Backpropagation is one of the methods contained in a neural network that is able to train dynamic networks using mathematical knowledge based on architectural models that have been developed in detail and systematically. Backpropagation itself is able to accommodate a lot of information that serves as a useful experience. However, the Backpropagation Algorithm tends to be slow to achieve convergence in obtaining optimum accuracy and requires large training data and the optimization used is less efficient. The purpose of this research is to optimize the learning rate on backpropagation neural networks. Source of data obtained from CV. Bona Tani Hatonduhan. There are 3 network architecture models used in this study, namely 2-51, 2-6-1, and 2-7-1 with learning rates of 0.1, 0.2, and 0.3. the results of trials carried out with MATLAB software produced the best architectural model, namely the 2-7-1 model with a learning rate of 0.3 with an accuracy of 83%. Based on this background, it is hoped that the results of the research can help in the process by optimizing the learning rate of the backpropagation Neural Network on the selection of the best architecture.
Co-Authors Agus Perdana Windarto Dedy Hartama Heru Satria Tambunan Irfan Sudahri Damani, Irfan Sudahri Okprana, Harly Wanto, Anjar